The Design of Single Moving Object Detection and Recognition System Based on OpenCV

Detection and recognition of moving objects is an important research direction in the field of computer vision. This paper proposes a new Frequency-tuned (FT) algorithm for extracting target dynamic saliency information from a mixture of Gaussian models, aiming at the inconspicuous effect of the traditional Frequency-tuned (FT) algorithm saliency map and the significant “dilution” of feature map fusion. This algorithm makes innovative improvements from distance metrics and feature graphs. In order to solve the large computational complexity of traditional identification algorithms, the algorithm uses a Haar cascaded classifier with low computational complexity as a classification algorithm, and uses OpenCV and Qt interface library to build an integrated multi-module system software platform to achieve single-target moving object detection and recognition. The experimental results show that the system has significant effects on the detection and recognition of single-target motion and has high accuracy, and it has a good engineering application prospect.